Conventionally, edge features are often used to classify whether a given grayscale image contains a human being. For example, a feature of histogram of edge directions HOG (Histograms of Oriented Gradients) is proposed for object identification (see Patent Literature 1 through 3, for example).
For an grayscale image I of M×N (M: wide, N: height), suppose the number of edge directions is Ni. A certain number of binary edge direction images Ei (i=1, . . . , Ni) are generated from I. Here, Ei is defined by the following equation.(Equation 1)Ei(x,y)=1, if the edge direction at position (x,y) is i. Ei(x,y)=0, otherwise  [1]
Where i is the edge direction number, i=1, . . . , Ni,                Ni: Total number of edge direction        
An edge direction histogram (HOG) is defined by the following equation.
                    (                  Equation          ⁢                                          ⁢          2                )                                                                      H          ⁡                      (            i            )                          =                              1            NM                    ⁢                                    ∑                              x                =                0                                            M                -                1                                      ⁢                                          ∑                                  y                  =                  0                                                  N                  -                  1                                            ⁢                                                E                  i                                ⁡                                  (                                      x                    ,                    y                                    )                                                                                        [        2        ]            
i: edge direction number, i=1, . . . , Ni 
Ni: total number of edge directions
Using an edge direction histogram enables the edge image in FIG. 1A and the edge image in FIG. 1B to be discriminated. That is to say, using an edge direction histogram enables images with different edge directions to be discriminated.
However, with an identification method using an edge direction histogram, statistical features of an entire image are calculated, and therefore discrimination between FIG. 1B and FIG. 1C, for example, is difficult. This is because the accumulation of edge directions is the same in the edge image in FIG. 1B and the edge image in FIG. 1C. As a result, false recognition occurs.
Thus, the use of edge co-occurrence has been proposed as one method capable of differentiating between the edge image in FIG. 1B and the edge image in FIG. 1C (see Non-Patent Literature 1, for example). This method uses a co-occurrence matrix of edge direction as a feature indicating edge co-occurrence. Co-occurrence matrix of edge direction HCO(i,j) is represented by the following equation.
                    (                  Equation          ⁢                                          ⁢          3                )                                                                                  H            co                    ⁡                      (                          i              ,              j                        )                          =                              1            NM                    ⁢                                    ∑                              x                =                0                                            M                -                1                                      ⁢                                          ∑                                  y                  =                  0                                                  N                  -                  1                                            ⁢                              [                                                                            E                      i                                        ⁡                                          (                                              x                        ,                        y                                            )                                                        ⁢                                                            ∑                                                                        (                                                                                    x                              ′                                                        ,                                                          y                              ′                                                                                )                                                ∈                                                  R                          ⁡                                                      (                                                          x                              ,                              y                                                        )                                                                                                                ⁢                                                                  E                        j                                            ⁡                                              (                                                                              x                            ′                                                    ,                                                      y                            ′                                                                          )                                                                                            ]                                                                        [        3        ]            
i, j: Edge direction number, i=1, . . . , Ni, j=1, . . . , Nj 
Ni, Nj: Total number of edge directions
R(x, y): Neighboring area of (x, y)
With an image identification method that uses edge direction co-occurrence, an edge direction within a neighboring area is taken into consideration in performing identification, and therefore correct identification is possible even for an edge image that cannot be correctly classified using only edge direction accumulation, as in the case of an edge direction histogram.